VLDB 2026 Research / reviewers in the wild / expert
Kaijie Guo
dblp:170/2419
· DBLP profile ↗
4ranked-venue papers
1as first author
4since 2021 · last 2025
—ORCID · conflict
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 3 · 1 first-author · 3 since 2021Artificial intelligence and machine learning · 1 · 1 since 2021
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | To PRI or Not To PRI, That's the question
Yun Wang 0039, Xianting Tian, Ben Luo, Zhixiang Wei, Zhibai Huang, Kailiang Xu, Kaihuan Peng, Kaijie Guo, Guangjian Wang, Shengdong Dai, Yibin Shen, Jiesheng Wu, Zhengwei Qi |
OSDI | 10 |
| 2025 | Tai Chi: A General High-Efficiency Scheduling Framework for SmartNICs in Hyperscale CloudsabstractCloud service providers increasingly adopt SmartNICs to offload data-plane services (e.g., DPDK and SPDK) and control-plane tasks (such as disk and NIC initialization). Our analysis of production environments reveals that data-plane services statically provision CPUs for peak load, resulting in 67.5% idle CPU cycles during 99% of their runtime in IaaS clouds, leading to wasted CPU resources. On the other hand, control-plane tasks fail to meet critical Service Level Objectives (SLOs), such as virtual machine startup time. Unfortunately, achieving control-plane SLO improvements through co-scheduling with idle data-plane services remains highly challenging, due to the combined effects of intrinsic scheduling latency and the substantial architectural complexity inherent to control-plane ecosystems. Bang Di, Kaijie Guo, Yibin Shen, Sanchuan Cheng, Fudong Qiu, Xiaokang Hu, Naixuan Guan, Dongdong Huang, Jinhu Li, Yi Wang 0004, Yifang Yang, Yilong Lv, Zhenwei Lu, Jiesheng Wu |
SOSP | 3 |
| 2025 | Remaining useful-life prediction of lithium battery based on neural-network ensemble via conditional variational autoencoder
Hengshan Zhang, Kaijie Guo, Yanping Chen 0006, Jiaze Sun |
Appl. Intell. | 2 |
| 2024 | VPRI: Efficient I/O Page Fault Handling via Software-Hardware Co-Design for IaaS CloudsabstractDevice pass-through has been widely adopted by cloud service providers to achieve near bare-metal I/O performance in virtual machines (VMs). However, this approach requires static pinning of VM memory, making on-demand paging unavailable. The hardware device I/O page fault (IOPF) capability offers an optimal solution to this limitation. Current IOPF approaches, using either standard IOMMU capabilities (ATS+PRI) or devices with independent IOMMU implementations, have not gained widespread adoption in public Infrastructure-as-a-Service clouds. This is due to high costs, platform dependency, and significant impacts on performance and service level objectives (SLOs). We present the Virtualized Page Request Interface (VPRI), a novel IOPF system developed through software-hardware collaboration. VPRI is not only platform-independent, free from address translation complexities, but also cost-effective, and designed to minimize SLO impact. Our work enables large-scale deployment of IOPF capability in Alibaba Cloud with negligible impact on SLOs. When integrated with memory management software, it significantly enhances memory utilization in public IaaS clouds, effectively overcoming the static memory pinning restriction associated with pass-through devices. Kaijie Guo, Dingji Li, Ben Luo, Yibin Shen, Kaihuan Peng, Ning Luo 0003, Shengdong Dai, Jianming Song, Zeyu Mi |
SOSP | 1 |